Machine learning for predicting psychotic relapse at 2 years in schizophrenia in the national FACE-SZ cohort.
Aggressiveness
Machine learning
Prediction
Relapse
Schizophrenia
Journal
Progress in neuro-psychopharmacology & biological psychiatry
ISSN: 1878-4216
Titre abrégé: Prog Neuropsychopharmacol Biol Psychiatry
Pays: England
ID NLM: 8211617
Informations de publication
Date de publication:
08 06 2019
08 06 2019
Historique:
received:
15
10
2018
revised:
27
11
2018
accepted:
10
12
2018
pubmed:
16
12
2018
medline:
30
6
2019
entrez:
16
12
2018
Statut:
ppublish
Résumé
Predicting psychotic relapse is one of the major challenges in the daily care of schizophrenia. To determine the predictors of psychotic relapse and follow-up withdrawal in a non-selected national sample of stabilized community-dwelling SZ subjects with a machine learning approach. Participants were consecutively included in the network of the FondaMental Expert Centers for Schizophrenia and received a thorough clinical and cognitive assessment, including recording of current treatment. Relapse was defined by at least one acute psychotic episode of at least 7 days, reported by the patient, her/his relatives or by the treating psychiatrist, within the 2-year follow-up. A classification and regression tree (CART) was used to construct a predictive decision tree of relapse and follow-up withdrawal. Overall, 549 patients were evaluated in the expert centers at baseline and 315 (57.4%) (mean age = 32.6 years, 24% female gender) were followed-up at 2 years. On the 315 patients who received a visit at 2 years, 125(39.7%) patients had experienced psychotic relapse at least once within the 2 years of follow-up. High anger (Buss&Perry subscore), high physical aggressiveness (Buss&Perry scale subscore), high lifetime number of hospitalization in psychiatry, low education level, and high positive symptomatology at baseline (PANSS positive subscore) were found to be the best predictors of relapse at 2 years, with a percentage of correct prediction of 63.8%, sensitivity 71.0% and specificity 44.8%. High PANSS excited score, illness duration <2 years, low Buss&Perry hostility score, high CTQ score, low premorbid IQ and low medication adherence (BARS) score were found to be the best predictors of follow-up withdrawal with a percentage of correct prediction of 52.4%, sensitivity 62%, specificity 38.7%. Machine learning can help constructing predictive score. In the present sample, aggressiveness appears to be a good early warning sign of psychotic relapse and follow-up withdrawal and should be systematically assessed in SZ subjects. The other above-mentioned clinical variables may help clinicians to improve the prediction of psychotic relapse at 2 years.
Sections du résumé
BACKGROUND
Predicting psychotic relapse is one of the major challenges in the daily care of schizophrenia.
OBJECTIVES
To determine the predictors of psychotic relapse and follow-up withdrawal in a non-selected national sample of stabilized community-dwelling SZ subjects with a machine learning approach.
METHODS
Participants were consecutively included in the network of the FondaMental Expert Centers for Schizophrenia and received a thorough clinical and cognitive assessment, including recording of current treatment. Relapse was defined by at least one acute psychotic episode of at least 7 days, reported by the patient, her/his relatives or by the treating psychiatrist, within the 2-year follow-up. A classification and regression tree (CART) was used to construct a predictive decision tree of relapse and follow-up withdrawal.
RESULTS
Overall, 549 patients were evaluated in the expert centers at baseline and 315 (57.4%) (mean age = 32.6 years, 24% female gender) were followed-up at 2 years. On the 315 patients who received a visit at 2 years, 125(39.7%) patients had experienced psychotic relapse at least once within the 2 years of follow-up. High anger (Buss&Perry subscore), high physical aggressiveness (Buss&Perry scale subscore), high lifetime number of hospitalization in psychiatry, low education level, and high positive symptomatology at baseline (PANSS positive subscore) were found to be the best predictors of relapse at 2 years, with a percentage of correct prediction of 63.8%, sensitivity 71.0% and specificity 44.8%. High PANSS excited score, illness duration <2 years, low Buss&Perry hostility score, high CTQ score, low premorbid IQ and low medication adherence (BARS) score were found to be the best predictors of follow-up withdrawal with a percentage of correct prediction of 52.4%, sensitivity 62%, specificity 38.7%.
CONCLUSION
Machine learning can help constructing predictive score. In the present sample, aggressiveness appears to be a good early warning sign of psychotic relapse and follow-up withdrawal and should be systematically assessed in SZ subjects. The other above-mentioned clinical variables may help clinicians to improve the prediction of psychotic relapse at 2 years.
Identifiants
pubmed: 30552914
pii: S0278-5846(18)30805-4
doi: 10.1016/j.pnpbp.2018.12.005
pii:
doi:
Types de publication
Journal Article
Multicenter Study
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
8-18Informations de copyright
Copyright © 2018 Elsevier Inc. All rights reserved.